Patient risk stratification for hospital-associated C. diff as a time-series classification task
A patient's risk for adverse events is affected by temporal processes including the nature and timing of diagnostic and therapeutic activities, and the overall evolution of the patient's pathophysiology over time. Yet many investigators ignore this temporal aspect when modeling patient out...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Neural Information Processing Systems Foundation, Inc
2021
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| Online Access: | https://hdl.handle.net/1721.1/129391 |
| _version_ | 1811073041234395136 |
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| author | Wiens, Jenna Anne Marleau Guttag, John V Horvitz, Eric |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Wiens, Jenna Anne Marleau Guttag, John V Horvitz, Eric |
| author_sort | Wiens, Jenna Anne Marleau |
| collection | MIT |
| description | A patient's risk for adverse events is affected by temporal processes including the nature and timing of diagnostic and therapeutic activities, and the overall evolution of the patient's pathophysiology over time. Yet many investigators ignore this temporal aspect when modeling patient outcomes, considering only the patient's current or aggregate state. In this paper, we represent patient risk as a time series. In doing so, patient risk stratification becomes a time-series classification task. The task differs from most applications of time-series analysis, like speech processing, since the time series itself must first be extracted. Thus, we begin by defining and extracting approximate risk processes, the evolving approximate daily risk of a patient. Once obtained, we use these signals to explore different approaches to time-series classification with the goal of identifying high-risk patterns. We apply the classification to the specific task of identifying patients at risk of testing positive for hospital acquired Clostridium difficile. We achieve an area under the receiver operating characteristic curve of 0.79 on a held-out set of several hundred patients. Our two-stage approach to risk stratification outperforms classifiers that consider only a patient's current state (p<0.05). |
| first_indexed | 2024-09-23T09:27:20Z |
| format | Article |
| id | mit-1721.1/129391 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:27:20Z |
| publishDate | 2021 |
| publisher | Neural Information Processing Systems Foundation, Inc |
| record_format | dspace |
| spelling | mit-1721.1/1293912022-09-26T11:32:32Z Patient risk stratification for hospital-associated C. diff as a time-series classification task Wiens, Jenna Anne Marleau Guttag, John V Horvitz, Eric Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory A patient's risk for adverse events is affected by temporal processes including the nature and timing of diagnostic and therapeutic activities, and the overall evolution of the patient's pathophysiology over time. Yet many investigators ignore this temporal aspect when modeling patient outcomes, considering only the patient's current or aggregate state. In this paper, we represent patient risk as a time series. In doing so, patient risk stratification becomes a time-series classification task. The task differs from most applications of time-series analysis, like speech processing, since the time series itself must first be extracted. Thus, we begin by defining and extracting approximate risk processes, the evolving approximate daily risk of a patient. Once obtained, we use these signals to explore different approaches to time-series classification with the goal of identifying high-risk patterns. We apply the classification to the specific task of identifying patients at risk of testing positive for hospital acquired Clostridium difficile. We achieve an area under the receiver operating characteristic curve of 0.79 on a held-out set of several hundred patients. Our two-stage approach to risk stratification outperforms classifiers that consider only a patient's current state (p<0.05). 2021-01-12T21:59:43Z 2021-01-12T21:59:43Z 2012 2019-05-30T13:53:44Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/129391 Wiens, Jenna et al. "Patient risk stratification for hospital-associated C. diff as a time-series classification task."Advances in Neural Information Processing Systems 25 (NIPS 2012), December 2012, Lake Tahoe, Nevada, Neural Information Processing Systems Foundation, 2012. en https://papers.nips.cc/paper/4525-patient-risk-stratification-for-hospital-associated-c-diff-as-a-time-series-classification-task Advances in Neural Information Processing Systems 25 (NIPS 2012) Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Neural Information Processing Systems Foundation, Inc Neural Information Processing Systems (NIPS) |
| spellingShingle | Wiens, Jenna Anne Marleau Guttag, John V Horvitz, Eric Patient risk stratification for hospital-associated C. diff as a time-series classification task |
| title | Patient risk stratification for hospital-associated C. diff as a time-series classification task |
| title_full | Patient risk stratification for hospital-associated C. diff as a time-series classification task |
| title_fullStr | Patient risk stratification for hospital-associated C. diff as a time-series classification task |
| title_full_unstemmed | Patient risk stratification for hospital-associated C. diff as a time-series classification task |
| title_short | Patient risk stratification for hospital-associated C. diff as a time-series classification task |
| title_sort | patient risk stratification for hospital associated c diff as a time series classification task |
| url | https://hdl.handle.net/1721.1/129391 |
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